Exploring Online Help-Seeking Tendencies: The Influence of Experience Type and Help Provider Type

Businesses are increasingly providing online assistance through help features to enhance the user experience in the digital era. Understanding factors influencing users’ tendencies to seek online help is crucial for optimizing resources and improving support and the overall user experience. Drawing on utilitarian- and hedonic-motivation systems theory, this paper examines how the type of experience and the type of help provider impact users’ online help-seeking behavior. Across three studies—including secondary data analysis, an online experiment, and an observational study of actual user behavior—we found that users are more (vs. less) inclined to seek help when encountering difficulties in utilitarian (vs. hedonic) experiences. This pattern was driven by users’ greater focus on achieving specific outcomes in utilitarian contexts, in contrast to their emphasis on experiential enjoyment in hedonic contexts. Importantly, users showed a stronger preference for seeking assistance from human agents over service robots when facing challenges in utilitarian experiences. Nevertheless, during hedonic experiences, no notable difference between humans and robots emerged in users’ inclination to seek help. These findings highlight the importance of considering the type of experience and the type of help provider when planning online support services, contributing valuable insights to the literature on hedonic vs. utilitarian motivation systems, users’ online help-seeking behavior, and user experience.

Authors

Institutions

Publication Details

Journal
MIS Quarterly
Published
2025-09-18
DOI
https://doi.org/10.25300/misq/2025/19133
Primary Topic
Technology Adoption and User Behaviour
Type
article
Field-Weighted Citation Impact
0.00
Controls
|||
ALL TIME
JAN
FEB
MAR
APR
MAY
JUN
JUL
AUG
SEP
OCT
article

Exploring Online Help-Seeking Tendencies: The Influence of Experience Type and Help Provider Type

Shaobo Li, Xun Huang, Xin Robert Luo, Le Wang et al.
MIS Quarterly
Technology Adoption and User Behaviour
article

Exploring Online Help-Seeking Tendencies: The Influence of Experience Type and Help Provider Type

Shaobo Li, Xun Huang, Xin Robert Luo, Le Wang, Yuwei Jiang
article en

Abstract

Businesses are increasingly providing online assistance through help features to enhance the user experience in the digital era. Understanding factors influencing users’ tendencies to seek online help is crucial for optimizing resources and improving support and the overall user experience. Drawing on utilitarian- and hedonic-motivation systems theory, this paper examines how the type of experience and the type of help provider impact users’ online help-seeking behavior. Across three studies—including secondary data analysis, an online experiment, and an observational study of actual user behavior—we found that users are more (vs. less) inclined to seek help when encountering difficulties in utilitarian (vs. hedonic) experiences. This pattern was driven by users’ greater focus on achieving specific outcomes in utilitarian contexts, in contrast to their emphasis on experiential enjoyment in hedonic contexts. Importantly, users showed a stronger preference for seeking assistance from human agents over service robots when facing challenges in utilitarian experiences. Nevertheless, during hedonic experiences, no notable difference between humans and robots emerged in users’ inclination to seek help. These findings highlight the importance of considering the type of experience and the type of help provider when planning online support services, contributing valuable insights to the literature on hedonic vs. utilitarian motivation systems, users’ online help-seeking behavior, and user experience.

MIS QuarterlyVol. 50(1)
Hong Kong Polytechnic University (HK), City University of Hong Kong (HK), University of New Mexico (US), Southern University of Science and Technology (CN), Decision Sciences (United States) (US), Metropolitan University (BD)
Openalex Percentile: Top 75%
Technology Adoption and User Behaviour
AI Navigator

Ask Laika to Summarize, Analyze, and Connect papers live on the map.

Summarize Papers & Methodologies

Extract key findings, datasets, and comparative methods across publications.

Benchmark Rankings & Visual Analytics

Rank top research institutions, authors, funders, topics, and journals by Field-Weighted Citation Impact (FWCI) and paper volume with instant charts.

Connect Distant Disciplines

Bridge topological clusters on the map to find hidden collaborative intersections.